import pandas as pd
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
# from machineLearn.bikePredict.calc import calcLR, calcRidge, calcLasso
dataFilePath = r'C:\Users\yun\Desktop\Bike-Sharing-Dataset\day.csv'
df = pd.read_csv(dataFilePath)
df.head()
df.info
df.shape
df.describe()
fig=plt.figure()
sns.distplot(df["temp"],bins=30,kde=True)

categorical_feature={'season','mnth','weathersit','weekday'}
for col in categorical_feature:
    print('\n%s属性的不同取值和出现的次数'%col)
    print(df[col].value_counts())
	
sns.violinplot(data=df[['yr','cnt']],x="yr",y="cnt")